1. Field
This disclosure generally relates to the field of video data processing. More particularly, the disclosure relates to Context Adaptive Binary Arithmetic Coding (“CABAC”) for digital video encoders.
2. General Background
Video signals generally include data corresponding to one or more video frames. Each video frame is composed of an array of picture elements, which are called pixels. A typical color video frame having a standard resolution may be composed of over several hundreds of thousands of pixels, which are arranged in arrays of blocks. Each pixel is characterized by pixel data indicative of a hue (predominant color), saturation (color intensity), and luminance (color brightness). The hue and saturation characteristics may be referred to as the chrominance. Accordingly, the pixel data includes chrominance and luminance. Therefore, the pixel data may be represented by groups of four luminance pixel blocks and two chrominance pixel blocks. These groups are called macroblocks (“MBs”). As a video frame generally includes many pixels, the video frame also includes a large number of MBs. Thus, digital signals representing a sequence of video frame data, which usually include many video frames, have a large number of bits. However, the available storage space and bandwidth for transmitting these digital signals is limited. Therefore, compression processes are used to more efficiently transmit or store video data.
Compression of digital video signals for transmission or for storage has become widely practiced in a variety of contexts. For example, multimedia environments for video conferencing, video games, Internet image transmissions, digital TV, and the like utilize compression. Coding and decoding are accomplished with coding processors. Examples of such coding processors include general computers, special hardware, multimedia boards, or other suitable processing devices. Further, the coding processors may utilize one of a variety of coding techniques, such as variable length coding (“VLC”), fixed coding, Huffman coding, blocks of symbols coding, and arithmetic coding. An example of arithmetic coding is Context Adaptive Binary Arithmetic Coding (“CABAC”).
CABAC techniques are capable of losslessly compressing syntax elements in a video stream using the probabilities of syntax elements in a given context. The CABAC process will take in syntax elements representing all elements within a macroblock. Further, the CABAC process constructs a compress bit sequence by building out the following structure: the sequential set of fields for the macroblock based on the chosen macroblock configuration, the specific syntax element type and value for each of the fields within this field sequence, and the context address for each of the syntax elements. The CABAC process will then perform binarization of the syntax elements, update the context weights, arithmetically encode the binarizations of syntax elements (“bins”), and subsequently pack the bits into bytes through the syntax element processing component.
The components of the CABAC process include: the CABAC weight initialization mode selection module, the macroblock syntax sequence generator, the binarization engine, the context address generator, the context weight update engine, the arithmetic coder, the bit packetizer, and the Network Abstraction Layer (“NAL”) header generator. The CABAC engine within a video encoder may accomplish two goals within the encoding process: (1) to carry out compressed data resource prediction for mode decision purposes; and (2) to losslessly compress the data for signal output delivery. The compressed data resource prediction task predicts the amount of bits required given a set of specific encoding modes for a given macroblock. Potential mode decision implementations may have up to eight modes to select from. The computational demand on the CABAC engine to support the mode decision task is significant.
The weight update, arithmetic encoder and the bit packing components of the CABAC engine may require a significant amount of non-trivial computational and processing resources in a sequential processor implementation. Given that high performance encoding systems require multiple macro block rate distortion iterations of encoding per macro block, the CABAC process may impose an unreasonable resource demand on a processor-based solution. Prior implementations typically compromise on mode decision CABAC resource estimation accuracy by limiting the CABAC to bin level accuracy.
A system capable of processing one binary symbol per clock cycle requires a matching back end-receiving engine capable of also processing the results on every cycle. The back end tasks consist of a value normalization task, which may generate up to eight bits of data, and a bit packing task, which groups the bits into bytes. The implementation solutions for the normalization and bit packing tasks are complex and computationally demanding.
Current implementations of the normalization function for the CABAC arithmetic coder fall into two categories. The first category includes routines that can generate at most one bit per cycle. This approach may utilize up to eight cycles to process one binary symbol as a single binary symbol may generate up to eight bits. The second category includes routines that achieve single cycle per binary symbol using a method that does not optimally handle all cases of the carry from the input data and the adder.